![Page 1: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/1.jpg)
What, When and Why of MongoDB
Solution Architect, MongoDB Inc.
Massimo Brignoli
@mongodb
![Page 2: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/2.jpg)
Agenda
About MongoDB Inc.
Data and Query Model
Scalability
Availability
Deployment Architectures
Schema Design Challenges
Use Cases
![Page 3: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/3.jpg)
About MongoDB
![Page 4: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/4.jpg)
MongoDB Inc. Overview
300+ employees 600+ customers
Offices in New York, Palo Alto, Washington DC, London, Dublin,
Barcelona and SydneyOver $231 million in funding
![Page 5: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/5.jpg)
6,000,000+ MongoDB Downloads
100,000+ Online Education Registrants
20,000+ MongoDB User Group Members
20,000+ MongoDB Days Attendees
15,000+ MongoDB Management Service (MMS) Users
Global Community
![Page 6: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/6.jpg)
MongoDB Inc. Products and Services
TrainingOnline and In-Person for Developers and Administrators
MongoDB Monitoring ServiceCloud-Based Service for Monitoring, Alerts, Backup and Restore
SubscriptionsMongoDB Enterprise, On-Prem Monitoring, Professional Support and Commercial License
ConsultingExpert Resources for All Phases of MongoDB Implementations
![Page 7: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/7.jpg)
Data & Query Model
![Page 8: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/8.jpg)
Operational Database Landscape
![Page 9: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/9.jpg)
Document Data Model
Relational MongoDB
{ first_name: ‘Paul’, surname: ‘Miller’, city: ‘London’, location: [45.123,47.232], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } }}
![Page 10: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/10.jpg)
Document Model Benefits
• Agility and flexibility– Data models can evolve easily– Companies can adapt to changes quickly
• Intuitive, natural data representation– Developers are more productive– Many types of applications are a good fit
• Reduces the need for joins, disk seeks– Programming is more simple– Performance can be delivered at scale
![Page 11: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/11.jpg)
Developers are more productive
![Page 12: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/12.jpg)
Developers are more productive
![Page 13: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/13.jpg)
Developers are more productive
![Page 14: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/14.jpg)
MongoDB is full featured
MongoDBRich Queries
• Find Paul’s cars• Find everybody in London with a
car built between 1970 and 1980
Geospatial• Find all of the car owners within
5km of Trafalgar Sq.
Text Search• Find all the cars described as
having leather seats
Aggregation• Calculate the average value of
Paul’s car collection
Map Reduce• What is the ownership pattern of
colors by geography over time? (is purple trending up in China?)
{ first_name: ‘Paul’, surname: ‘Miller’, city: ‘London’, location: [45.123,47.232], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } }}
![Page 15: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/15.jpg)
Shell and Drivers
Shell
Command-line shell for
interacting directly with
database
DriversDrivers for most popular programming languages and frameworks
> db.collection.insert({company:“10gen”, product:“MongoDB”})> > db.collection.findOne(){
“_id” : ObjectId(“5106c1c2fc629bfe52792e86”),
“company” : “10gen”“product” : “MongoDB”
}
Java
Python
Perl
Ruby
Haskell
JavaScript
![Page 16: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/16.jpg)
Scalability
![Page 17: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/17.jpg)
Automatic Sharding
• Three types of sharding: hash-based, range-based, tag-aware
• Increase or decrease capacity as you go
• Automatic balancing
![Page 18: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/18.jpg)
Query Routing
• Multiple query optimization models
• Each sharding option appropriate for different apps
![Page 19: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/19.jpg)
Availability
![Page 20: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/20.jpg)
High Availability – Ensure application availability
during many types of failures
Disaster Recovery – Address the RTO and RPO goals
for business continuity
Maintenance – Perform upgrades and other
maintenance operations with no application downtime
Availability Considerations
![Page 21: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/21.jpg)
Replica Sets
• Replica Set – two or more copies
• “Self-healing” shard
• Addresses many concerns:
- High Availability
- Disaster Recovery
- Maintenance
![Page 22: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/22.jpg)
Replica Set Benefits
Business Needs Replica Set Benefits
High Availability Automated failover
Disaster Recovery Hot backups offsite
Maintenance Rolling upgrades
Low Latency Locate data near users
Workload Isolation Read from non-primary replicas
Data Privacy Restrict data to physical location
Data Consistency Tunable Consistency
![Page 23: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/23.jpg)
Deployment Architectures
![Page 24: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/24.jpg)
Single Data Center
• Automated failover
• Tolerates server failures
• Tolerates rack failures
• Number of replicas defines failure tolerance
Primary – A Primary – B Primary – C
Secondary – A
Secondary – A
Secondary – B
Secondary – B
Secondary – C
Secondary – C
![Page 25: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/25.jpg)
Active/Standby Data Center
• Tolerates server and rack failure
• Standby data center
Data Center - West
Primary – A Primary – B Primary – C
Secondary – A
Secondary – B
Secondary – C
Data Center - East
Secondary – A
Secondary – B
Secondary – C
![Page 26: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/26.jpg)
Active/Active Data Center
• Tolerates server, rack, data center failures, network partitions
Data Center - West
Primary – A Primary – B Primary – C
Secondary – A
Secondary – B
Secondary – C
Data Center - East
Secondary – A
Secondary – B
Secondary – C
Secondary – B
Secondary – C
Secondary – A
Data Center - Central
Arbiter – A Arbiter – B Arbiter – C
![Page 27: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/27.jpg)
Global Data Distribution
Real-time
Real-time Real-time
Real-time
Real-time
Real-time
Real-time
Primary
Secondary
Secondary
Secondary
Secondary
Secondary
Secondary
Secondary
![Page 28: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/28.jpg)
Read Global/Write Local
Primary:NYC
Secondary:NYC
Primary:LON
Primary:SYD
Secondary:LON
Secondary:NYC
Secondary:SYD
Secondary:LON
Secondary:SYD
![Page 29: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/29.jpg)
Schema Design Challenges
![Page 30: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/30.jpg)
First a story:
Once upon a time there was a medical records company…
![Page 31: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/31.jpg)
![Page 32: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/32.jpg)
![Page 33: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/33.jpg)
![Page 34: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/34.jpg)
![Page 35: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/35.jpg)
![Page 36: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/36.jpg)
Schema Design Challenge
• Flexibility– Easily adapt to new requirements
• Agility– Rapid application development
• Scalability– Support large data and query volumes
![Page 37: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/37.jpg)
Schema Design:
MongoDB vs. Relational
![Page 38: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/38.jpg)
MongoDB Relational
Collections Tables
Documents Rows
Data Use Data Storage
What questions do I have?
What answers do I have?
MongoDB versus Relational
![Page 39: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/39.jpg)
Attribute MongoDB Relational
Storage N-dimensional Two-dimensional
Field Values0, 1, many, or embed
Single value
QueryAny field or level
Any field
Schema Flexible Very structured
Updates In line In place
![Page 40: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/40.jpg)
With relational, this is hard
Long development times
Inflexible
Doesn’t scale
![Page 41: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/41.jpg)
Document model is much easier
Shorter development times
Flexible
Scalable
{ "patient_id": "1177099", "first_name": "John", "last_name": "Doe", "middle_initial": "A", "dob": "2000-01-25", "gender": "Male", "blood_type": "B+", "address": "123 Elm St., Chicago, IL 59923", "height": "66", "weight": "110", "allergies": ["Nuts", "Penicillin", "Pet Dander"], "current_medications": [{"name": "Zoloft", "dosage": "2mg", "frequency": "daily", "route": "orally"}], "complaint" : [{"entered": "2000-11-03", "onset": "2000-11-03", "prob_desc": "", "icd" : 250.00, "status" : "Active"}, {"entered": "2000-02-04", "onset": "2000-02-04", "prob_desc": "in spite of regular exercise, ...", "icd" : 401.9, "status" : "Active"}], "diagnosis" : [{"visit" : "2005-07-22" , "narrative" : "Fractured femur", "icd" : "9999", "priority" : "Primary"}, {"visit" : "2005-07-22" , "narrative" : "Type II Diabetes", "icd" : "250.00", "priority" : "Secondary"}]}
![Page 42: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/42.jpg)
Let’s model something together
How about a business card?
![Page 43: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/43.jpg)
Business Card
![Page 44: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/44.jpg)
Address Book Entity-Relationship
Contacts• name• company• title
Addresses
• type• street• city• state• zip_code
Phones• type• number
Emails• type• address
Thumbnails
• mime_type
• dataPortraits• mime_typ
e• data
Groups• name
N
1
N
1
N
N
N
1
1
1
11
Twitters• name• location• web• bio
1
1
![Page 45: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/45.jpg)
Referencing
Contact
• name• compan
y• title• phone
Address
• street• city• state• zip_cod
e
Use two collections with a reference
Similar to relational
![Page 46: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/46.jpg)
Contact
• name• company• address
• Street• City• State• Zip
• title• phone
• address• street• city• State• zip_cod
e
Embedding
Document Schema
![Page 47: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/47.jpg)
Referencing
Contacts
{
“_id”: 2,
“name”: “Steven Jobs”,
“title”: “VP, New Product Development”,
“company”: “Apple Computer”,
“phone”: “408-996-1010”,
“address_id”: 1
}
Addresses
{“_id”: 1,“street”: “10260 Bandley Dr”,“city”: “Cupertino”,“state”: “CA”,“zip_code”: ”95014”,“country”: “USA”
}
![Page 48: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/48.jpg)
EmbeddingContacts
{
“_id”: 2,
“name”: “Steven Jobs”,
“title”: “VP, New Product Development”,
“company”: “Apple Computer”,
“address”: {“street”: “10260 Bandley Dr”,
“city”: “Cupertino”,
“state”: “CA”,
“zip_code”: ”95014”,
“country”: “USA”},
“phone”: “408-996-1010”
}
![Page 49: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/49.jpg)
How are they different? Why?
Contact
• name• compan
y• title• phone
Address
• street• city• state• zip_cod
e
Contact
• name• company• adress
• Street• City• State• Zip
• title• phone
• address• street• city• state• zip_cod
e
![Page 50: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/50.jpg)
Schema Flexibility{
“name”: “Steven Jobs”,“title”: “VP, New Product
Development”,“company”: “Apple
Computer”,“address”: {
“street”: 10260 Bandley Dr”,
“city”: “Cupertino”,“state”: “CA”,“zip_code”:
“95014”},“phone”: “408-996-1010”
}
{“name”: “Larry Page,“url”: “http://google.com”,“title”: “CEO”,“company”: “Google!”,“address”: {
“street”: 555 Bryant, #106”,
“city”: “Palo Alto”,“state”: “CA”,“zip_code”:
“94301”},“phone”: “650-330-0100”“fax”: ”650-330-1499”
}
![Page 51: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/51.jpg)
One-to-many embedding vs. referencing
{ “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “[email protected]”, “address”: [{ “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301” }] “phones”: [{“type”: “Office”, “number”: “650-618-1499”}, {“type”: “fax”, “number”: “650-330-0100”}]}
{ “name”: “Larry Page”, “url”: “http://google.com/”, “title”: “CEO”, “company”: “Google!”, “email”: “[email protected]”, “address”: [“addr99”], “phones”: [“ph23”, “ph49”]}
{ “_id”: “addr99”, “street”: “555 Bryant, #106”, “city”: “Palo Alto”, “state”: “CA”, “zip_code”: “94301”}
{ “_id”: “ph23”, “type”: “Office”, “number”: “650-618-1499”},{ “_id”: “ph49”,
“type”: “fax”, “number”: “650-330-0100”}
![Page 52: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/52.jpg)
Many to ManyTraditional Relational Association
Join tableContacts
namecompanytitlephone
Groupsname
GroupContacts
group_idcontact_idX
Use arrays instead
![Page 53: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/53.jpg)
Address Book Entity-Relationship
Contacts• name• company• title
Addresses
• type• street• city• state• zip_code
Phones• type• number
Emails• type• address
Thumbnails
• mime_type
• dataPortraits• mime_typ
e• data
Groups• name
N
1
N
1
N
N
N
1
1
1
11
Twitters• name• location• web• bio
1
1
![Page 54: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/54.jpg)
Contacts• name• company• title
addresses• type• street• city• state• zip_code
phones• type• number
emails• type• address
thumbnail• mime_type• data
Portraits• mime_type• data
Groups• name
N
1
N
1
twitter• name• location• web• bio
N
N
N
1
1
Document model - holistic and efficient representation
![Page 55: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/55.jpg)
Contact document example{
“name” : “Gary J. Murakami, Ph.D.”,“company” : “MongoDB, Inc”,“title” : “Lead Engineer and Ruby Evangelist”,“twitter” : {
“name” : “GaryMurakami”, “location” : “New Providence, NJ”,“web” : “http://www.nobell.org”
},“portrait_id” : 1,“addresses” : [
{ “type” : “work”, “street” : ”229 W 43rd St.”, “city” : “New York”, “zip_code” : “10036” }
],“phones” : [
{ “type” : “work”, “number” : “1-866-237-8815 x8015” }],“emails” : [
{ “type” : “work”, “address” : “[email protected]” },{ “type” : “home”, “address” : “[email protected]” }
]}
![Page 56: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/56.jpg)
Health Care Use Cases
![Page 57: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/57.jpg)
360-Degree Patient View
• Healthcare provider networks have massive amounts of patient data
– Both structured and unstructured– Basic patient informations– Lab results– MRI images
• Centralization of data needed– Aggregation of all the data in one repository
• Analytics
![Page 58: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/58.jpg)
Population Management for At-Risk Demographics
• Certain populations are known to be prone to certain diseases.
• Analyzing data insurers help people take preventative measures
– reminding them to get regularly scheduled colonoscopies
• Help insurers to reduce costs and to expand margins,
![Page 59: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/59.jpg)
Lab Data Management and Analytics
• Strain on traditional technological systems:– Rise of number of tests conducted– Rise of variety of data collected– Lack of flexibility
• With MongoDB’s flexible data model, providers of lab testing, genomics and clinical pathology can:
– Ingest, store and analyze a variety of data types– Coming from numerous sources all in a single data
store
• enables these companies to generate new insights and revenue streams
![Page 60: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/60.jpg)
Other use cases for MongoDB in healthcare include:
• Fraud Detection
• Remote Monitoring and Body Area Networks
• Mobile Apps for Doctors and Nurses
• Pandemic Detection with Real-Time Geospatial Analytics
• Electronic Healthcare Records (EHR)
• Advanced Auditing Systems for Compliance
• Hospital Equipment Management and Optimization
![Page 62: tranSMART Community Meeting 5-7 Nov 13 - Session 2: MongoDB: What, Why And When](https://reader035.vdocuments.site/reader035/viewer/2022062511/54bbf1de4a795952248b456f/html5/thumbnails/62.jpg)